Mining Geoscience Text from the Geological Survey of Queensland for hidden Geo-Resource evidence

Text mining algorithms were used to discover hidden geo-resource (metals, elements, minerals) associations in reports, maps, sketches and logs from the archives of the Geological Survey of Queensland in Australia. The Geological Survey of Queensland have made a number of excellent improvements recently increasing the accessibility of these data.

A subset of report packages over the past 40 years manually tagged to hydrocarbons were analysed using the OpportunityFinder® algorithm equating to over 2 Million sentences. Using Natural Language Processing (NLP), Knowledge Engineering and Machine Learning, locational information, Chronostratigraphy and Lithostratigraphy were automatically extracted along with co-occurrence data.

Indicator mineral evidence (as well as direct evidence) for critical resources were detected (such as Rare Earth Elements (REE), Gold, Silver, Copper and Nickel), despite not being mentioned in the petroleum report package metadata. These were ranked by several factors including speculation.

The Geological Survey of Queensland (GSQ) is the state’s custodian of geoscience knowledge and data. GSQ collects and provides geoscience data, information and advice about Queensland’s mineral and energy resources and resource potential.

Infoscience Technologies Limited is an Artificial Intelligence tech start-up, extracting geoscience knowledge from unstructured text.

The heatmap chart below shows co-occurrences between minerals driven from the text, clustered automatically using Pearson’s. This may produce interesting associations that may warrant further investigation.

#geoscience #subsurface #mining #minerals #digitaltransformation #naturallanguageprocessing #machinelearning #textanalytics


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